Overview

Dataset statistics

Number of variables14
Number of observations4320
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory472.6 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

lifetime is highly overall correlated with purchase_countHigh correlation
recency is highly overall correlated with avg_purchase_interval and 2 other fieldsHigh correlation
avg_purchase_interval is highly overall correlated with recency and 2 other fieldsHigh correlation
frequency is highly overall correlated with recency and 2 other fieldsHigh correlation
number_products is highly overall correlated with nunique_products and 2 other fieldsHigh correlation
nunique_products is highly overall correlated with number_products and 2 other fieldsHigh correlation
avg_basket_size is highly overall correlated with avg_order_value and 1 other fieldsHigh correlation
purchase_count is highly overall correlated with lifetime and 8 other fieldsHigh correlation
charge_back_count is highly overall correlated with purchase_count and 1 other fieldsHigh correlation
return_rate is highly overall correlated with purchase_count and 1 other fieldsHigh correlation
avg_order_value is highly overall correlated with avg_basket_size and 1 other fieldsHigh correlation
gross_revenue is highly overall correlated with number_products and 4 other fieldsHigh correlation
avg_unt_price is highly skewed (γ1 = 36.54224156)Skewed
gross_revenue is highly skewed (γ1 = 21.5077176)Skewed
customer_id has unique valuesUnique
charge_back_count has 2828 (65.5%) zerosZeros
return_rate has 2828 (65.5%) zerosZeros

Reproduction

Analysis started2023-05-29 18:33:12.610778
Analysis finished2023-05-29 18:33:46.608470
Duration34 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct4320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15300.771
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:46.778435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12617.95
Q113815.75
median15299.5
Q316778.25
95-th percentile17980.2
Maximum18287
Range5940
Interquartile range (IQR)2962.5

Descriptive statistics

Standard deviation1720.2179
Coefficient of variation (CV)0.11242688
Kurtosis-1.1947082
Mean15300.771
Median Absolute Deviation (MAD)1481
Skewness0.0010717092
Sum66099331
Variance2959149.6
MonotonicityNot monotonic
2023-05-29T15:33:47.358793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
15280 1
 
< 0.1%
15700 1
 
< 0.1%
17299 1
 
< 0.1%
12837 1
 
< 0.1%
15076 1
 
< 0.1%
17444 1
 
< 0.1%
15921 1
 
< 0.1%
15747 1
 
< 0.1%
15840 1
 
< 0.1%
Other values (4310) 4310
99.8%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
12357 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18281 1
< 0.1%
18280 1
< 0.1%
18278 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%

lifetime
Real number (ℝ)

Distinct305
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.07708
Minimum0
Maximum373
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:47.549546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25
Q1113
median249
Q3326
95-th percentile371
Maximum373
Range373
Interquartile range (IQR)213

Descriptive statistics

Standard deviation117.7561
Coefficient of variation (CV)0.52787178
Kurtosis-1.2272183
Mean223.07708
Median Absolute Deviation (MAD)102
Skewness-0.36980487
Sum963693
Variance13866.499
MonotonicityDecreasing
2023-05-29T15:33:47.740662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373 95
 
2.2%
372 91
 
2.1%
366 83
 
1.9%
368 70
 
1.6%
369 69
 
1.6%
365 67
 
1.6%
358 58
 
1.3%
367 50
 
1.2%
371 46
 
1.1%
359 42
 
1.0%
Other values (295) 3649
84.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 4
 
0.1%
2 5
0.1%
3 9
0.2%
4 7
0.2%
5 5
0.1%
7 6
0.1%
8 3
 
0.1%
9 5
0.1%
10 12
0.3%
ValueCountFrequency (%)
373 95
2.2%
372 91
2.1%
371 46
1.1%
369 69
1.6%
368 70
1.6%
367 50
1.2%
366 83
1.9%
365 67
1.6%
364 40
0.9%
362 31
 
0.7%

recency
Real number (ℝ)

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.013657
Minimum0
Maximum373
Zeros35
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:47.934243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median50
Q3142
95-th percentile311
Maximum373
Range373
Interquartile range (IQR)125

Descriptive statistics

Standard deviation100.06908
Coefficient of variation (CV)1.0875459
Kurtosis0.43240872
Mean92.013657
Median Absolute Deviation (MAD)40
Skewness1.2467806
Sum397499
Variance10013.82
MonotonicityNot monotonic
2023-05-29T15:33:48.114428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 103
 
2.4%
4 94
 
2.2%
3 94
 
2.2%
2 90
 
2.1%
8 79
 
1.8%
10 77
 
1.8%
17 74
 
1.7%
7 71
 
1.6%
9 70
 
1.6%
22 64
 
1.5%
Other values (294) 3504
81.1%
ValueCountFrequency (%)
0 35
 
0.8%
1 103
2.4%
2 90
2.1%
3 94
2.2%
4 94
2.2%
5 48
1.1%
7 71
1.6%
8 79
1.8%
9 70
1.6%
10 77
1.8%
ValueCountFrequency (%)
373 17
0.4%
372 17
0.4%
371 6
 
0.1%
369 3
 
0.1%
368 5
 
0.1%
367 5
 
0.1%
366 10
0.2%
365 10
0.2%
364 6
 
0.1%
362 6
 
0.1%

avg_purchase_interval
Real number (ℝ)

Distinct1402
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.597846
Minimum0
Maximum373
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:48.306779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.736549
Q131
median60
Q3119
95-th percentile289.05
Maximum373
Range373
Interquartile range (IQR)88

Descriptive statistics

Standard deviation84.740189
Coefficient of variation (CV)0.94578377
Kurtosis2.0189487
Mean89.597846
Median Absolute Deviation (MAD)34
Skewness1.6162457
Sum387062.7
Variance7180.8996
MonotonicityNot monotonic
2023-05-29T15:33:48.488060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 31
 
0.7%
30 26
 
0.6%
25 23
 
0.5%
106 21
 
0.5%
18 21
 
0.5%
64 20
 
0.5%
23 19
 
0.4%
29 19
 
0.4%
43 19
 
0.4%
92 18
 
0.4%
Other values (1392) 4103
95.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
1 5
0.1%
1.534979424 1
 
< 0.1%
1.718894009 1
 
< 0.1%
2 3
0.1%
2.207100592 1
 
< 0.1%
2.25 1
 
< 0.1%
2.5 1
 
< 0.1%
2.96031746 1
 
< 0.1%
ValueCountFrequency (%)
373 15
0.3%
372 14
0.3%
371 5
 
0.1%
369 1
 
< 0.1%
368 4
 
0.1%
367 5
 
0.1%
366 6
 
0.1%
365 8
0.2%
364 5
 
0.1%
362 6
 
0.1%

frequency
Real number (ℝ)

Distinct1402
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029348771
Minimum0
Maximum1.5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:48.685261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0034482759
Q10.0084033613
median0.016666667
Q30.032258065
95-th percentile0.085110323
Maximum1.5
Range1.5
Interquartile range (IQR)0.023854703

Descriptive statistics

Standard deviation0.056479631
Coefficient of variation (CV)1.9244292
Kurtosis219.29335
Mean0.029348771
Median Absolute Deviation (MAD)0.0098820059
Skewness12.05582
Sum126.78669
Variance0.0031899487
MonotonicityNot monotonic
2023-05-29T15:33:48.871205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01886792453 31
 
0.7%
0.03333333333 26
 
0.6%
0.04 23
 
0.5%
0.009433962264 21
 
0.5%
0.05555555556 21
 
0.5%
0.015625 20
 
0.5%
0.04347826087 19
 
0.4%
0.03448275862 19
 
0.4%
0.02325581395 19
 
0.4%
0.01086956522 18
 
0.4%
Other values (1392) 4103
95.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.002680965147 15
0.3%
0.002688172043 14
0.3%
0.00269541779 5
 
0.1%
0.0027100271 1
 
< 0.1%
0.002717391304 4
 
0.1%
0.00272479564 5
 
0.1%
0.002732240437 6
 
0.1%
0.002739726027 8
0.2%
0.002747252747 5
 
0.1%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1 5
0.1%
0.6514745308 1
 
< 0.1%
0.581769437 1
 
< 0.1%
0.5 3
 
0.1%
0.4530831099 1
 
< 0.1%
0.4444444444 1
 
< 0.1%
0.4 1
 
< 0.1%
0.3378016086 1
 
< 0.1%
0.3333333333 10
0.2%

number_products
Real number (ℝ)

Distinct468
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.728241
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:49.070105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median41
Q3100
95-th percentile315.05
Maximum7838
Range7837
Interquartile range (IQR)83

Descriptive statistics

Standard deviation228.81283
Coefficient of variation (CV)2.4944643
Kurtosis482.47675
Mean91.728241
Median Absolute Deviation (MAD)30
Skewness18.091345
Sum396266
Variance52355.309
MonotonicityNot monotonic
2023-05-29T15:33:49.261758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 85
 
2.0%
6 77
 
1.8%
9 75
 
1.7%
1 70
 
1.6%
15 69
 
1.6%
11 68
 
1.6%
8 67
 
1.6%
5 66
 
1.5%
7 65
 
1.5%
28 65
 
1.5%
Other values (458) 3613
83.6%
ValueCountFrequency (%)
1 70
1.6%
2 50
1.2%
3 56
1.3%
4 48
1.1%
5 66
1.5%
6 77
1.8%
7 65
1.5%
8 67
1.6%
9 75
1.7%
10 85
2.0%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

nunique_products
Real number (ℝ)

Distinct341
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.584954
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:49.456102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median35
Q377.25
95-th percentile204
Maximum1786
Range1785
Interquartile range (IQR)61.25

Descriptive statistics

Standard deviation85.403084
Coefficient of variation (CV)1.3867524
Kurtosis99.614722
Mean61.584954
Median Absolute Deviation (MAD)24
Skewness6.9167284
Sum266047
Variance7293.6867
MonotonicityNot monotonic
2023-05-29T15:33:49.634323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 91
 
2.1%
10 85
 
2.0%
8 80
 
1.9%
9 79
 
1.8%
11 76
 
1.8%
13 76
 
1.8%
6 73
 
1.7%
5 72
 
1.7%
15 72
 
1.7%
14 72
 
1.7%
Other values (331) 3544
82.0%
ValueCountFrequency (%)
1 91
2.1%
2 51
1.2%
3 60
1.4%
4 51
1.2%
5 72
1.7%
6 73
1.7%
7 71
1.6%
8 80
1.9%
9 79
1.8%
10 85
2.0%
ValueCountFrequency (%)
1786 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
884 1
< 0.1%
817 1
< 0.1%
717 1
< 0.1%
714 1
< 0.1%
699 1
< 0.1%
636 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct2243
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.08462
Minimum0.25
Maximum7824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:49.828012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile30
Q180
median140
Q3236.15833
95-th percentile524.01667
Maximum7824
Range7823.75
Interquartile range (IQR)156.15833

Descriptive statistics

Standard deviation269.78265
Coefficient of variation (CV)1.3483427
Kurtosis193.52202
Mean200.08462
Median Absolute Deviation (MAD)70.630682
Skewness10.017756
Sum864365.58
Variance72782.679
MonotonicityNot monotonic
2023-05-29T15:33:50.016081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 19
 
0.4%
72 18
 
0.4%
64 17
 
0.4%
144 16
 
0.4%
136 16
 
0.4%
44 16
 
0.4%
60 15
 
0.3%
146 15
 
0.3%
100 15
 
0.3%
88 15
 
0.3%
Other values (2233) 4158
96.2%
ValueCountFrequency (%)
0.25 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
1 2
 
< 0.1%
2 4
0.1%
3 4
0.1%
3.333333333 1
 
< 0.1%
4 7
0.2%
5 3
0.1%
5.25 1
 
< 0.1%
5.5 1
 
< 0.1%
ValueCountFrequency (%)
7824 1
< 0.1%
4300 1
< 0.1%
4280 1
< 0.1%
3206.083333 1
< 0.1%
3028 1
< 0.1%
2924 1
< 0.1%
2880 1
< 0.1%
2708 1
< 0.1%
2692.547945 1
< 0.1%
2529 1
< 0.1%

purchase_count
Real number (ℝ)

Distinct63
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0310185
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:50.210413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile16
Maximum243
Range242
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.1323251
Coefficient of variation (CV)1.815204
Kurtosis222.19401
Mean5.0310185
Median Absolute Deviation (MAD)2
Skewness11.431686
Sum21734
Variance83.399362
MonotonicityNot monotonic
2023-05-29T15:33:50.399282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1301
30.1%
2 800
18.5%
3 488
 
11.3%
4 381
 
8.8%
5 282
 
6.5%
6 195
 
4.5%
7 152
 
3.5%
8 117
 
2.7%
9 80
 
1.9%
10 73
 
1.7%
Other values (53) 451
 
10.4%
ValueCountFrequency (%)
1 1301
30.1%
2 800
18.5%
3 488
 
11.3%
4 381
 
8.8%
5 282
 
6.5%
6 195
 
4.5%
7 152
 
3.5%
8 117
 
2.7%
9 80
 
1.9%
10 73
 
1.7%
ValueCountFrequency (%)
243 1
< 0.1%
217 1
< 0.1%
169 1
< 0.1%
126 1
< 0.1%
118 2
< 0.1%
88 1
< 0.1%
75 1
< 0.1%
73 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%

charge_back_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77199074
Minimum0
Maximum45
Zeros2828
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:50.563506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9735699
Coefficient of variation (CV)2.5564684
Kurtosis151.42598
Mean0.77199074
Median Absolute Deviation (MAD)0
Skewness9.1742875
Sum3335
Variance3.8949782
MonotonicityNot monotonic
2023-05-29T15:33:50.704675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 2828
65.5%
1 839
 
19.4%
2 288
 
6.7%
3 139
 
3.2%
4 92
 
2.1%
5 37
 
0.9%
6 32
 
0.7%
7 21
 
0.5%
9 8
 
0.2%
10 5
 
0.1%
Other values (13) 31
 
0.7%
ValueCountFrequency (%)
0 2828
65.5%
1 839
 
19.4%
2 288
 
6.7%
3 139
 
3.2%
4 92
 
2.1%
5 37
 
0.9%
6 32
 
0.7%
7 21
 
0.5%
8 5
 
0.1%
9 8
 
0.2%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
35 1
 
< 0.1%
27 1
 
< 0.1%
21 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
0.1%

return_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct143
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.099820228
Minimum0
Maximum0.75
Zeros2828
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:50.874295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile0.5
Maximum0.75
Range0.75
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.16131425
Coefficient of variation (CV)1.6160478
Kurtosis1.0390432
Mean0.099820228
Median Absolute Deviation (MAD)0
Skewness1.4736402
Sum431.22338
Variance0.026022289
MonotonicityNot monotonic
2023-05-29T15:33:51.056685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2828
65.5%
0.5 234
 
5.4%
0.3333333333 211
 
4.9%
0.25 165
 
3.8%
0.2 139
 
3.2%
0.1666666667 78
 
1.8%
0.1428571429 61
 
1.4%
0.4 55
 
1.3%
0.125 46
 
1.1%
0.2857142857 40
 
0.9%
Other values (133) 463
 
10.7%
ValueCountFrequency (%)
0 2828
65.5%
0.01369863014 1
 
< 0.1%
0.02222222222 1
 
< 0.1%
0.02272727273 1
 
< 0.1%
0.02631578947 1
 
< 0.1%
0.02857142857 1
 
< 0.1%
0.0303030303 1
 
< 0.1%
0.03225806452 1
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03448275862 1
 
< 0.1%
ValueCountFrequency (%)
0.75 1
 
< 0.1%
0.7142857143 1
 
< 0.1%
0.6666666667 18
 
0.4%
0.6 13
 
0.3%
0.5714285714 5
 
0.1%
0.5555555556 2
 
< 0.1%
0.5454545455 2
 
< 0.1%
0.5 234
5.4%
0.4736842105 1
 
< 0.1%
0.4615384615 4
 
0.1%

avg_unt_price
Real number (ℝ)

Distinct4173
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4633073
Minimum0.1225
Maximum434.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:51.254624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1225
5-th percentile1.3236833
Q12.1573676
median2.8290222
Q33.7120688
95-th percentile5.9766905
Maximum434.65
Range434.5275
Interquartile range (IQR)1.5547011

Descriptive statistics

Standard deviation8.875852
Coefficient of variation (CV)2.5628255
Kurtosis1578.4644
Mean3.4633073
Median Absolute Deviation (MAD)0.74558104
Skewness36.542242
Sum14961.487
Variance78.780749
MonotonicityNot monotonic
2023-05-29T15:33:51.428207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.06 10
 
0.2%
1.79 8
 
0.2%
4.95 7
 
0.2%
2.08 7
 
0.2%
1.25 6
 
0.1%
2.55 6
 
0.1%
2.95 6
 
0.1%
12.75 4
 
0.1%
4.25 4
 
0.1%
0.72 4
 
0.1%
Other values (4163) 4258
98.6%
ValueCountFrequency (%)
0.1225 1
< 0.1%
0.17 2
< 0.1%
0.2327777778 1
< 0.1%
0.29 2
< 0.1%
0.32 1
< 0.1%
0.358 1
< 0.1%
0.3666666667 1
< 0.1%
0.39 1
< 0.1%
0.3917241379 1
< 0.1%
0.39375 1
< 0.1%
ValueCountFrequency (%)
434.65 1
< 0.1%
295 1
< 0.1%
125 1
< 0.1%
110 2
< 0.1%
74.975 1
< 0.1%
66.475 1
< 0.1%
59.73333333 1
< 0.1%
54.3 1
< 0.1%
51.71 1
< 0.1%
32.97142857 1
< 0.1%

avg_order_value
Real number (ℝ)

Distinct4250
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.33512
Minimum0.96666667
Maximum9904.875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:51.608823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.96666667
5-th percentile76.497444
Q1154.895
median240.79536
Q3372.115
95-th percentile775.86938
Maximum9904.875
Range9903.9083
Interquartile range (IQR)217.22

Descriptive statistics

Standard deviation390.52051
Coefficient of variation (CV)1.1966855
Kurtosis136.13431
Mean326.33512
Median Absolute Deviation (MAD)100.78464
Skewness8.8462255
Sum1409767.7
Variance152506.27
MonotonicityNot monotonic
2023-05-29T15:33:51.796995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 4
 
0.1%
76.32 4
 
0.1%
113.5 3
 
0.1%
15 3
 
0.1%
197.9 3
 
0.1%
35.4 3
 
0.1%
440 3
 
0.1%
72 2
 
< 0.1%
142.4 2
 
< 0.1%
357.21 2
 
< 0.1%
Other values (4240) 4291
99.3%
ValueCountFrequency (%)
0.9666666667 1
 
< 0.1%
3.75 2
< 0.1%
5.9 1
 
< 0.1%
6.12 1
 
< 0.1%
9.14 1
 
< 0.1%
11.67 1
 
< 0.1%
12.75 1
 
< 0.1%
15 3
0.1%
17 1
 
< 0.1%
20.475 1
 
< 0.1%
ValueCountFrequency (%)
9904.875 1
< 0.1%
6207.67 1
< 0.1%
5383.975 1
< 0.1%
5151.590833 1
< 0.1%
4873.81 1
< 0.1%
4669.19 1
< 0.1%
4366.78 1
< 0.1%
4327.621667 1
< 0.1%
4314.72 1
< 0.1%
4131.233333 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4245
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1920.743
Minimum2.9
Maximum278778.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-05-29T15:33:51.975549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile110.7975
Q1300.845
median654.76
Q31611.4325
95-th percentile5657.141
Maximum278778.02
Range278775.12
Interquartile range (IQR)1310.5875

Descriptive statistics

Standard deviation8320.8119
Coefficient of variation (CV)4.3320798
Kurtosis595.75815
Mean1920.743
Median Absolute Deviation (MAD)453.69
Skewness21.507718
Sum8297609.7
Variance69235911
MonotonicityNot monotonic
2023-05-29T15:33:52.144762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.32 4
 
0.1%
440 3
 
0.1%
35.4 3
 
0.1%
15 3
 
0.1%
113.5 3
 
0.1%
363.65 3
 
0.1%
144 2
 
< 0.1%
161 2
 
< 0.1%
598.2 2
 
< 0.1%
248.61 2
 
< 0.1%
Other values (4235) 4293
99.4%
ValueCountFrequency (%)
2.9 1
 
< 0.1%
3.75 1
 
< 0.1%
5.9 1
 
< 0.1%
12.24 1
 
< 0.1%
12.75 1
 
< 0.1%
15 3
0.1%
17 1
 
< 0.1%
20.8 2
< 0.1%
25.5 1
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
278778.02 1
< 0.1%
259657.3 1
< 0.1%
189735.53 1
< 0.1%
133007.13 1
< 0.1%
123638.18 1
< 0.1%
114505.32 1
< 0.1%
88138.2 1
< 0.1%
65920.12 1
< 0.1%
62924.1 1
< 0.1%
59419.34 1
< 0.1%

Interactions

2023-05-29T15:33:43.876413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:13.193565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:15.636895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:18.102180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:20.397285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:22.848881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:25.126965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:27.589579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:30.069144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:32.413311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:34.657889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:37.120745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:39.429731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:41.642796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:44.025263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:13.350454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:15.834353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:18.253497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:20.553609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:23.000799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:25.290345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:27.749842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:30.236676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:32.620444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:34.856493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:37.277621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:39.574394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:41.800156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:44.182417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:13.520813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:16.002336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:18.413209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:20.718477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:23.162816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:25.459987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:27.915081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:30.409736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:32.776272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:35.018258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:37.497079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:39.726403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:41.963204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:44.340391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:13.682526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:16.166060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:18.637459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:20.882590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:23.324361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:25.627886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:28.075261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:30.589363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:32.946648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:35.180771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:37.669547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:39.934941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:42.133664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:44.497804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:13.849301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:16.329960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:18.800967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:21.049447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:23.485936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:25.813222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:28.240709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:30.761775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:33.102925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:35.336826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:37.835730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:40.089074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:42.308110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:44.675744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:14.020030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:16.498740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:18.966738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:21.216307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:23.647255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:26.031537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:28.405445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:30.936310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:33.269483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:35.493294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:37.999455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:40.247542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:42.483013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:44.849186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:14.197064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:16.670940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:19.138440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:21.390996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:23.811891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:26.207757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:28.572817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:31.111365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:33.434663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:35.656536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:38.173364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:40.408287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:42.665660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:45.016795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:14.402060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:16.987859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:19.302794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:21.562969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:23.978345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:26.382473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:28.948039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:31.282925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:33.596178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:35.815902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:38.336289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:40.564397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:42.822853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:45.181863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:14.607618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:17.156284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:19.472013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:21.732011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:24.151891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:26.557023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:29.115462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:31.453414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:33.763196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:35.979648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:38.508186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:40.725178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:42.998235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:45.332938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:14.819539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:17.320485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:19.624206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:21.882186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:24.303194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:26.721722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:29.267716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:31.610334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:33.908677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:36.130599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:38.661601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:40.893044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:43.146652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:45.473061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:14.975102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:17.469321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:19.772771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:22.030808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:24.454866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:26.875787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:29.414226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:31.769980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:34.055550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:36.270987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:38.811937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:41.033616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:43.287081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:45.635239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:15.138502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:17.634777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:19.939224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:22.396124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:24.664624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:27.053632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:29.578010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:31.942783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:34.220768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:36.429659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:38.975525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:41.197113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:43.446350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:45.775337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:15.293437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:17.780471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:20.088985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:22.541524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:24.811186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:27.260520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:29.739781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:32.094700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:34.359318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:36.569633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:39.124375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:41.335706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:43.585196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:45.927275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:15.449754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:17.949014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:20.241708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:22.694259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:24.969146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:27.427209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:29.905695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:32.253415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:34.509115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:36.977267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:39.277282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:41.493140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T15:33:43.730603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-29T15:33:52.310399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customer_idlifetimerecencyavg_purchase_intervalfrequencynumber_productsnunique_productsavg_basket_sizepurchase_countcharge_back_countreturn_rateavg_unt_priceavg_order_valuegross_revenue
customer_id1.0000.0040.006-0.0010.002-0.005-0.009-0.087-0.013-0.044-0.051-0.018-0.105-0.079
lifetime0.0041.0000.1050.287-0.2850.3440.300-0.0530.5140.3070.2210.0940.0010.398
recency0.0060.1051.0000.764-0.763-0.499-0.456-0.151-0.536-0.272-0.1710.097-0.115-0.485
avg_purchase_interval-0.0010.2870.7641.000-0.999-0.462-0.416-0.116-0.550-0.443-0.3570.077-0.076-0.471
frequency0.002-0.285-0.763-0.9991.0000.4620.4160.1140.5510.4430.358-0.0760.0750.470
number_products-0.0050.344-0.499-0.4620.4621.0000.9830.3850.7160.4110.293-0.0840.3810.788
nunique_products-0.0090.300-0.456-0.4160.4160.9831.0000.3950.6420.3740.273-0.0810.3830.734
avg_basket_size-0.087-0.053-0.151-0.1160.1140.3850.3951.0000.057-0.050-0.094-0.4000.8170.512
purchase_count-0.0130.514-0.536-0.5500.5510.7160.6420.0571.0000.6670.5570.0260.0610.801
charge_back_count-0.0440.307-0.272-0.4430.4430.4110.374-0.0500.6671.0000.9580.080-0.0520.482
return_rate-0.0510.221-0.171-0.3570.3580.2930.273-0.0940.5570.9581.0000.087-0.1030.362
avg_unt_price-0.0180.0940.0970.077-0.076-0.084-0.081-0.4000.0260.0800.0871.0000.0020.021
avg_order_value-0.1050.001-0.115-0.0760.0750.3810.3830.8170.061-0.052-0.1030.0021.0000.611
gross_revenue-0.0790.398-0.485-0.4710.4700.7880.7340.5120.8010.4820.3620.0210.6111.000

Missing values

2023-05-29T15:33:46.151325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-29T15:33:46.474398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idlifetimerecencyavg_purchase_intervalfrequencynumber_productsnunique_productsavg_basket_sizepurchase_countcharge_back_countreturn_rateavg_unt_priceavg_order_valuegross_revenue
017850.0373.0372.010.6571430.093834297.021.048.371429351.00.0285713.960370151.1037145288.63
113047.0373.056.023.3125000.042895171.0105.084.687500167.00.4375003.926082193.0687503089.10
212583.0373.02.021.9411760.045576232.0114.0292.823529172.00.1176472.140474389.9611766629.34
313748.0373.095.074.6000000.01340528.024.087.80000050.00.0000003.996429189.650000948.25
415100.0373.0333.062.1666670.0160863.01.09.66666763.00.50000010.950000105.850000635.10
515291.0373.025.019.6315790.050938102.061.0109.105263195.00.2631584.702255239.5531584551.51
614688.0373.07.013.8148150.072386327.0148.0119.333333276.00.2222222.131040189.1622225107.38
717809.0373.016.026.6428570.03753461.046.0144.000000142.00.1428572.905738381.7750005344.85
815311.0373.00.03.1610170.3163542379.0567.0319.66101711827.00.2288142.506036503.55372959419.34
916098.0373.087.053.2857140.01876767.034.087.57142970.00.0000004.424627286.5185712005.63
customer_idlifetimerecencyavg_purchase_intervalfrequencynumber_productsnunique_productsavg_basket_sizepurchase_countcharge_back_countreturn_rateavg_unt_priceavg_order_valuegross_revenue
431016000.02.02.00.6666671.59.09.01703.33333330.00.03.3455564131.23333312393.70
431115195.02.02.02.0000000.51.01.01404.00000010.00.02.7500003861.0000003861.00
431214087.02.02.01.0000001.069.061.0125.00000021.00.51.42072590.835000181.67
431314204.02.02.02.0000000.544.036.082.00000010.00.02.405000161.030000161.03
431415471.02.02.02.0000000.577.067.0266.00000010.00.01.999221469.480000469.48
431513436.01.01.01.0000001.012.012.076.00000010.00.05.830000196.890000196.89
431615520.01.01.01.0000001.018.018.0314.00000010.00.01.724444343.500000343.50
431713298.01.01.01.0000001.02.02.096.00000010.00.03.750000360.000000360.00
431814569.01.01.01.0000001.012.010.079.00000010.00.03.920000227.390000227.39
431912713.00.00.00.0000000.037.037.0505.00000010.00.02.084595794.550000794.55